Development, Validation and Tool Support of a Stochastic Calculus for Networks with Flow Transformations

在过去的十年里，随机网络演算已经成为网络和分布式系统性能分析的经典排队论的一种通用的替代方法。它的前景是，它可以处理排队论根本难以处理的问题，这是基于它适用于(概率)界的事实，而不是努力寻求准确的解。现有排队分析方法的一个很大的挑战是处理具有流变换的排队网络，流变换是当流的数据在网络中被改变时发生的。流变换实际上是许多现代联网和分布式系统的特征，例如，出于能量效率的目的，无线传感器网络处理传输的数据，同时将其传送到汇聚节点。这个项目解决了随机网络演算的扩展，以处理流转换，并由三个相互关联的部分组成。在理论方面，该项目寻求开发随机比例元素，用于在随机网络演算的框架内对流量转换进行非常一般的建模。在应用方面，该项目试图将其理论应用于几个真实世界的应用场景，以验证其有用性和准确性。此外，该项目计划提供相应的软件工具，以便能够进行自动化分析，并使其他业绩分析员能够查阅理论结果。

Over the last decade, the stochastic network calculus has established itself as a versatile alternative methodology to the classical queueing theory for the performance analysis of networks and distributed systems. Its prospect is that it can deal with problems that are fundamentally hard for queueing theory based on the fact that it works with (probabilistic) bounds rather than striving for exact solutions. A great challenge of existing methodologies for queueing analysis is to deal with queueing networks subject to flow transformations, which occur when the flows’ data is altered inside the network. Flow transformations are in fact characteristic to many modern networked and distributed systems, e.g., a wireless sensor network processes the transported data, while delivering it to a sink node, for energy-efficiency purposes. This project addresses an extension of the stochastic network calculus to deal with flow transformations and comprises of three inter-connected parts. On the theoretical side, the project seeks to develop stochastic scaling elements, for modelling flow transformations in great generality, within the framework of the stochastic network calculus. On the application side, the project seeks to apply its theory to several real-world application scenarios in order to validate its usefulness as well as its accuracy. Furthermore, the project plans to deliver a corresponding software tool in order to allow for an automated analysis and to make the theoretical results accessible to other performance analysts.